Proceedings of the 3rd IKDD Conference on Data Science, 2016最新文献_第2页

Audience Prism: Segmentation and Early Classification of Visitors Based on Reading Interests 受众棱镜:基于阅读兴趣的受众细分与早期分类

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888459

Lilly Kumari, Sunny Dhamnani, Akshat Bhatnagar, Atanu R. Sinha, R. Sinha

{"title":"Audience Prism: Segmentation and Early Classification of Visitors Based on Reading Interests","authors":"Lilly Kumari, Sunny Dhamnani, Akshat Bhatnagar, Atanu R. Sinha, R. Sinha","doi":"10.1145/2888451.2888459","DOIUrl":"https://doi.org/10.1145/2888451.2888459","url":null,"abstract":"The largest Media and Entertainment (M&E) web portals today cater to more than 100 Million unique visitors every month. In Customer Relationship Management, customer segmentation plays an important role, with the goal of targeting different products for different segments. Marketers segment their customers based on customer attributes. In the non-subscription based media business, the customer is analogous to the visitor, the product to the content, and a purchase to consumption. Knowing which segment an audience member belongs to, enables better engagement. In this work, we address the problems: 1) How can we segment audience members of an M&E web property based on their media consumption interests? 2) When a new visitor arrives, how can we classify them into one of the above defined segments (without having to wait for consumption history)? We apply our proposed solution to a real world data-set and show that we can achieve coherent clusters and can predict cluster membership with a high level of accuracy. We also build a tool that the editors can find valuable towards understanding their audience.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124615212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Smart filters for social retrieval 用于社会检索的智能过滤器

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888457

Balaji Vasan Srinivasan, Tanya Goyal, N. M. Nainani, Kartik K. Sreenivasan

引用次数: 1

An Approach to Allocate Advertisement Slots for Banner Advertising 一种条幅广告的广告位分配方法

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888472

V. Kavya, P. Reddy

引用次数: 2

Consensus Clustering Approach for Discovering Overlapping Nodes in Social Networks 社会网络重叠节点发现的共识聚类方法

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888471

D. Shankar, S. Bhavani

引用次数: 1

Feature Creation based Slicing for Privacy Preserving Data Mining 基于特征创建的隐私保护数据挖掘切片

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888462

R. Priyadarsini, M. Valarmathi, S. Sivakumari

{"title":"Feature Creation based Slicing for Privacy Preserving Data Mining","authors":"R. Priyadarsini, M. Valarmathi, S. Sivakumari","doi":"10.1145/2888451.2888462","DOIUrl":"https://doi.org/10.1145/2888451.2888462","url":null,"abstract":"In the digital era vast amount of data are collected and shared for purpose of research and analysis. These data contain sensitive information about the people and organizations which needs to be protected during the process of data mining. This work proposes Feature Creation Based Slicing [FCBS] algorithm for preserving privacy such that sensitive data are not exposed during the process of data mining in Multi Trust Level [MTL] environment. The proposed algorithm applies three layers of privacy preservation using both perturbation and non-perturbation techniques and creates new features from already existing attribute vector. Experiments are performed on real life and benchmarked datasets and the results are compared with the existing slicing and L-diversity algorithms. The results show that privacy preserved datasets generated using the proposed algorithm yields negligible hiding failure while protecting sensitive patterns during association mining and gives comparable utility during classification. Due to feature creation process in the proposed algorithm, linking and known background attacks are prevented. Also, the variance values of the proposed privacy preserved datasets show that they can prevent diversity attacks.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134623794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Events Describe Places: Tagging Places with Event Based Social Network Data 事件描述地点:用基于事件的社会网络数据标记地点

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888477

Vinod Hegde, A. Mileo, A. Pozdnoukhov

{"title":"Events Describe Places: Tagging Places with Event Based Social Network Data","authors":"Vinod Hegde, A. Mileo, A. Pozdnoukhov","doi":"10.1145/2888451.2888477","DOIUrl":"https://doi.org/10.1145/2888451.2888477","url":null,"abstract":"Location based services and Geospatial web applications have become popular in recent years due to wide adoption of mobile devices. Search and recommendation of places or Points of Interests (PoIs) are prominent services available on them. The effectiveness of these services crucially depends on the availability of tags that are descriptive of places. The major geospatial databases that contain data about places suffer from the lack of descriptive tags for places, since writing them is a time-consuming process and only a few users do it despite having knowledge about places. In order to tackle this issue and automatically generate descriptive tags for places, we propose a solution that utilizes data about a set of events that happen in a specific place and use it to extract meaningful descriptive tags for that place. We use data about events held at places on Meetup, a well known event based social network and apply Latent Dirichlet Allocation (LDA) to derive sets of probable descriptive tags for any place. In order to evaluate our approach, we measure semantic relatedness between tags derived for places on Meetup and manually assigned tags from Foursquare, a location based service. Results show that event data can be used to derive semantically relevant place tags. This shows that location based services can benefit from capturing data about events to derive place tags.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129671726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Learning from Gurus: Analysis and Modeling of Reopened Questions on Stack Overflow 向大师学习:堆栈溢出重新开放问题的分析和建模

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888460

Rishabh Gupta, P. Reddy

引用次数: 6

AMEO 2015: A dataset comprising AMCAT test scores, biodata details and employment outcomes of job seekers AMEO 2015:一个包含AMCAT测试分数、求职者生物数据细节和就业结果的数据集

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2892037

V. Aggarwal, Shashank Srikant, Harsh Nisar

{"title":"AMEO 2015: A dataset comprising AMCAT test scores, biodata details and employment outcomes of job seekers","authors":"V. Aggarwal, Shashank Srikant, Harsh Nisar","doi":"10.1145/2888451.2892037","DOIUrl":"https://doi.org/10.1145/2888451.2892037","url":null,"abstract":"More than a million engineers enter the global workforce every year. A relevant question is what determines the jobs and salaries these engineers are offered right after graduation. Previous studies have shown the influence of various factors such as college reputation, grades, the field one specializes in and market conditions for specific industries. An important input which such analyses do not have is a standardized measures of job skills done at the time of completion of studies. We present here Aspiring Minds' Employability Outcomes 2015 (AMEO 2015), a unique dataset which provides engineering graduates' employment outcomes (salaries, job titles and job locations) together with standardized assessment scores in three fundamental areas - cognitive skills, technical skills and personality. Coupled with biodata information, AMEO 2015 provides an opportunity for a unique and comprehensive study of the entry level labor market. The data could be used to make an accurate salary predictor, but also understand what influences salary and job titles in the labor market. In this paper we describe the details of the dataset and discuss a spectrum of questions around meritocracy in labor markets, biases in labor selection and other prevalent market forces it can help uncover and answer. You can download the dataset at: http://research.aspiringminds.com/resources/","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117091361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Detecting Community Structures in Social Networks by Graph Sparsification 基于图稀疏的社交网络社区结构检测

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888479

Partha Basuchowdhuri, Satyaki Sikdar, Sonu Sreshtha, S. Majumder

{"title":"Detecting Community Structures in Social Networks by Graph Sparsification","authors":"Partha Basuchowdhuri, Satyaki Sikdar, Sonu Sreshtha, S. Majumder","doi":"10.1145/2888451.2888479","DOIUrl":"https://doi.org/10.1145/2888451.2888479","url":null,"abstract":"Community structures are inherent in social networks and finding them is an interesting and well-studied problem. Finding community structures in social networks is similar to locating densely connected clusters of nodes in a graph. One of the popular methods for finding communities is to first find the inter-community edges and then removing them to reveal the communities. It is well-known that a network centrality measure named edge betweenness can be used to detect the inter-community edges. The edges with high edge betweenness are those that fall in a large number of shortest paths out of all possible pairs of shortest paths. Finding all-pair shortest paths is a computationally expensive task, especially for large-sized graphs. So we construct a t-spanner, a known graph sparsification technique, for finding edges with high betweenness and eventually find communities by removing such edges. Using the t-spanner, we then detect the inter-community edges in O(km) running time by building a distance oracle of size O(kn1+1/k), where t = 2k-1. Compared to the traditional community detection methods dependent on calculation of betweenness values, our algorithm runs much faster. Experiments show that our algorithm finds communities of quality comparable to the other state-of-the-art community detection algorithms.","PeriodicalId":136431,"journal":{"name":"Proceedings of the 3rd IKDD Conference on Data Science, 2016","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Improving Urban Transportation through Social Media Analytics 通过社交媒体分析改善城市交通

Proceedings of the 3rd IKDD Conference on Data Science, 2016 Pub Date : 2016-03-13 DOI: 10.1145/2888451.2888478

Manjira Sinha, P. Varma, Gayatri Sivakumar, Mridula Singh, Tridib Mukherjee, D. Chander, K. Dasgupta

引用次数: 1